Sequencing Instrument Fault Isolation for Subcomponent Failure Detection
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Solution Overview
Problem
Existing sequencing instruments lack the ability to accurately identify the specific subcomponent causing malfunction, leading to incorrect replacements and significant operational losses due to technicians relying on overall sequencing confidence scores.
Innovation Solution
A machine learning-based system using a fault detection classifier and expert fault isolation rules to analyze time series data from sequencing runs, identifying patterns indicative of subcomponent failures through preprocessing and feature extraction, followed by a two-stage process to isolate the faulty component.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If technicians rely on overall sequencing confidence scores to identify malfunctions, then the simplicity of operation is maintained, but the precision of subcomponent identification deteriorates
Solution Approach 1:
The patent segments the overall sequencing system into multiple subsystems (optical, mechanical, fluidic, electronic) and further divides them into individual subcomponents. Each subcomponent has dedicated sensors that collect specific operational parameters, enabling precise identification of malfunctioning parts without requiring complex manual diagnosis by technicians.
Solution Approach 2:
The patent introduces an intermediary diagnostic system comprising sensors, data collectors, and analysis modules that mediate between the sequencing instrument and the technician. This intermediary automatically processes operational data, compares it against baseline patterns, and identifies malfunctioning subcomponents, thereby maintaining operational simplicity while achieving high identification precision.
2Reliability
If incorrect subcomponent replacement is performed, then the time for maintenance operations increases, but the reliability of the system deteriorates
Solution Approach 1:
The patent performs preliminary diagnostic actions by continuously monitoring operational parameters and detecting anomalies before they cause complete system failure. The system proactively identifies malfunctioning subcomponents and alerts operators in advance, allowing for scheduled maintenance during convenient time windows rather than emergency repairs that extend operational disruption.
Solution Approach 2:
The patent implements a feedback mechanism where sensor data from subcomponents is continuously collected, analyzed, and compared against baseline operational patterns. When deviations indicate malfunction, the system provides feedback to operators with specific diagnostic information about the faulty subcomponent, enabling rapid and accurate replacement decisions that reduce both maintenance time and improve reliability.
3Measurement precision
If detailed monitoring of all subcomponents is implemented, then the precision of fault detection is improved, but the device complexity increases
Solution Approach 1:
The patent employs universal sensor modules and data collection frameworks that can monitor multiple different subcomponent types using standardized interfaces and protocols. This multi-functional approach allows detailed monitoring of all subcomponents without proportionally increasing system complexity, as the same diagnostic infrastructure handles diverse sensor inputs from optical, mechanical, fluidic, and electronic subsystems.
Solution Approach 2:
The patent replaces complex manual diagnostic procedures with automated electronic monitoring and data analysis systems. Sensors, processors, and software algorithms substitute for technician expertise and manual inspection, enabling precise fault detection across all subcomponents while keeping the added complexity manageable through automation rather than manual processes.
Data Source
AI summary
The technology disclosed relates to systems and methods for diagnosing system malfunction and isolating a cause of system malfunction. The method includes applying preprocessors to time series data. The preprocessors detect time series discontinuities, drift, lack of expected correlation, and trends. The method includes feeding, for at least one image channel in one sequencing run, at least part of the output of the preprocessors to a trained tree-based classifier and receiving a classification of the particular sequencing run as abnormal. The abnormal classification can indicate a system malfunction. The method includes feeding at least part of the output of the preprocessors for the abnormal sequencing run to an expert rule system. The expert rule system can isolate a root cause of the system malfunction to a particular subcomponent in need of adjustment or replacement. The method can generate a notification of the particular subcomponent causing the system malfunction.